AI and Machine Learning Technology is used to create value for organizations. Through data integration and machine learning, deeper insights are enabled to identify new opportunities and achieve improved outcomes.

Some examples are:

Detection

Detecting anomalies can be incredibly difficult for humans trying to keep track of more data than they can handle, but an AI application can identify anomalies in data and automatically alert if something is out of the ordinary.

Optimization

AI applications built to optimize are trying to achieve a task or goal the best it can in the least amount of time. Based on what the AI observes, it will try to identify and replicate whatever actions have been taken that lead to the best responses.

Prediction

Prediction engines aim to extrapolate likely future results based an existing learning set of data. Prediction engines are useful for setting goals, analyzing application performance metrics, and predicting anomalies.

Personalization

AI/ML can help to personalize an application's user experience (UX) by learning from a user's past behavior and tailoring the app to display information, make suggestions, etc based on that behavior.

Classification

Classification applications can be very useful to sort different variables into different categories. For example, an AI application can search for keywords or phrases and recognize which are positive or negative.

Automation

Using AI to automate tasks is a common goal for individuals and organizations. If a simple, repeatable task can be automated by an AI application, it can save tremendous amounts of time and money.

Vision

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”